Description Usage Arguments Details Value
Iterative IPCW Update Procedure of Augmented Efficient Influence Function
1 2 3 4 5 6 7 8 9 10 11 12 13 | ipcw_eif_update(
data_internal,
C_samp,
V,
ipc_mech,
ipc_weights,
Qn_estim,
Hn_estim,
estimator = c("tmle", "onestep"),
fluctuation = NULL,
flucmod_tol = 50,
eif_reg_type = c("hal", "glm")
)
|
data_internal |
A |
C_samp |
A |
V |
A |
ipc_mech |
A |
ipc_weights |
A |
Qn_estim |
A |
Hn_estim |
A |
estimator |
The type of estimator to be fit, either |
fluctuation |
A |
flucmod_tol |
A |
eif_reg_type |
Whether a flexible nonparametric function ought to be
used in the dimension-reduced nuisance regression of the targeting step for
the censored data case. By default, the method used is a nonparametric
regression based on the Highly Adaptive Lasso (from hal9001). Set
this to |
An adaptation of the IPCW-TMLE for iteratively constructing an efficient inverse probability of censoring weighted TML or one-step estimator. The efficient influence function of the parameter and updating the IPC weights in an iterative process, until a convergence criteria is satisfied.
A list
containing the estimated outcome mechanism, the fitted
fluctuation model for TML updates, the updated inverse probability of
censoring weights (IPCW), the updated estimate of the efficient influence
function, and the estimated IPCW component of the EIF.
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